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cs.CR2025
TurboFuzzLLM: Turbocharging Mutation-based Fuzzing for Effectively Jailbreaking Large Language Models in Practice
Aman Goel, Xian Carrie Wu, Zhe Wang +2
Jailbreaking large-language models (LLMs) involves testing their robustness against adversarial prompts and evaluating their ability to withstand prompt attacks that could elicit u…
cs.CR2025
Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation
Daniel Schwartz, Dmitriy Bespalov, Zhe Wang +2
As large language models (LLMs) become increasingly prevalent, ensuring their robustness against adversarial misuse is crucial. This paper introduces the GAP (Graph of Attacks with…
cs.CR2024
TaeBench: Improving Quality of Toxic Adversarial Examples
Xuan Zhu, Dmitriy Bespalov, Liwen You +2
Toxicity text detectors can be vulnerable to adversarial examples - small perturbations to input text that fool the systems into wrong detection. Existing attack algorithms are tim…